xl Sawhney, 2000). eHubs create value by aggregating buyers and sellers, creating marketplace liquidity, and reducing transaction costs. :KLOHPDQ\¿UPVEHOLHYHHPDUNHWSODFHVZLOOEULQJEHQH¿WVPDQ\HPDUNHWSODFHVVXIIHUHGIURP WKHLULQDELOLW\WRJHQHUDWHVXI¿FLHQWUHYHQXHIURPWKLQWUDQVDFWLRQYROXPHV6XUYLYLQJHPDUNHWSODFHV are struggling to present compelling value propositions for participants. Hence, e-marketplaces need to LGHQWLI\SRWHQWLDOVRXUFHVRIYDOXHVWRVWD\LQWKHEXVLQHVVLQFOXGLQJHI¿FLHQWDOORFDWLRQRIUHVRXUFHV improved information collection and aggregation, quality service, and risk management. As e-business business models and technologies develop, the supply chain dimension of e-business, called e-supply chain, has become one of the most discussed topics in the various industries (Kim et al., 2005; Nath & Angeles, 2005). E-supply chain emerged as a promising alternative to traditional supply chain and dramatically changed the way procurement is conducted. E-supply chain also opens up new opportunities for SMEs, considering the traditional proprietary EDI technology was not available for most SMEs. A study of organizational barriers to e-supply chain integration revealed that (1) internal bar- riers impeded e-integration more than either upstream supplier barriers or downstream customer barriers would and (2) e-integration has a positive effect on the performance (Frohlich, 2002). These barriers to LPSOHPHQWDWLRQDQGRUJDQL]DWLRQDOLVVXHVQHHGWREHDGGUHVVHGWRUHDOL]HSRWHQWLDOEHQH¿WV (VXSSO\FKDLQHQKDQFHVVXSSO\FKDLQÀH[LELOLW\PXFKQHHGHGLQWLPHVRIK\SHUFRPSHWLWLYHEXVL- ness environments, sophisticated customer preferences, and widespread use of disruptive technologies. Based on a survey result of supply chain relationships in the IT industry, Gosain, et al. (2004) proposed two e-supply chain design principles: (1) modular design of interconnected processes and structured data connectivity, and (2) deep coordination-related knowledge. They also suggested that sharing a broad UDQJHRILQIRUPDWLRQZLWKSDUWQHUVLVFRXQWHUSURGXFWLYHWRVXSSO\FKDLQÀH[LELOLW\DQGWKDWRUJDQL]DWLRQV should instead enhance information quality. Business-to-Consumer (B2C) E-Commerce B2C e-commerce created many successful e-commerce startups with unique business models such as eBay, Amazon.com, and expedia.com. B2C e-commerce is characterized by intense competition, low market entry barriers, and a low degree of customer loyalty, which in part are the reason behind the de- mise of numerous B2C e-commerce startups in the late 1990s and early 2000s. Due to the rapid growth of the Internet population and online sales, most traditional retailers have been transformed into click and mortar organizations by establishing the B2C ecommerce web sites. For these B2C e-commerce organizations, understanding online consumer behavior is one of the most important tasks for their business success. To understand online consumer behavior, a number of researchers have applied grounded theories and investigated web site characteristics, motivations, facilitators, inhibitors, trust, attitude, intention, and loyalty which underlie individual acceptance of B2C e-commerce applications (Kulviwat et al., 2006; Jih, 2007). Most of these empirical studies adapted Theory of Planned Behavior (TPB), Technol- ogy Acceptance Model (TAM), and SERVQUAL which have been widely used in IT adoption studies. 7KHVHVWXGLHVVKRZHGWKDWFRQVXPHU¶VDFFHSWDQFHRIHFRPPHUFHLVLQÀXHQFHGE\VXEMHFWLYHQRUPV perceived usefulness, perceived site quality, risk, vendor quality, security, reliability, assurance, privacy, and user’s web experience. ,QDGGLWLRQWRWKHDERYHPHQWLRQHGHFRPPHUFHDFFHSWDQFHVWXGLHVDQXPEHURIVWXGLHVVSHFL¿- cally focused on trust issues in e-commerce (Bhattacherjee, 2006; Pollard & Diggles, 2006). Four main xli DQWHFHGHQWVDUHNQRZQWRLQÀXHQFHRQFRQVXPHUWUXVWLQ%&HFRPPHUFH/HH7XUEDQWUXVW- worthiness of the Internet merchant, trustworthiness of the Internet as a shopping medium, infrastructural FRQWH[WXDOIDFWRUVDQGRWKHUIDFWRUVVXFKDVFRPSDQ\VL]HDQGGHPRJUDSKLFYDULDEOHV7KHVH¿QGLQJV indicate that merchant integrity is a major positive determinant of consumer trust in online shopping, and that its effect is moderated by the individual consumer’s trust propensity. Psychological antecedents of LQVWLWXWLRQEDVHGWUXVWDUHFDWHJRUL]HGLQWR¿YHJURXSVRIIDFWRUVSHUVRQDOLW\EDVHGSHUFHSWLRQEDVHG attitude-based, experience-based, and knowledge-based (Walczuch & Lundgren, 2004). Perception-based factors were found to be the main determinants of consumer trust in e-retailing. As B2C e-commerce technologies advance and consumers gain more online experience, e-services have drawn attention from researchers and practitioners (Gefen & Straub, 2003; Baida et al., 2007). E-services enhance consumers’ online shopping experience, and include Internet radio, web-based de- cision support, personalization, e-payment, online inquiry, electronic document sharing, and e-product support services. One of the major drawbacks of e-services is that e-services frequently lack a social SUHVHQFH)LQGLQJVRIDIUHHVLPXODWLRQH[SHULPHQWVKRZHGWKHSRVLWLYHLQÀXHQFHRIVRFLDOSUHVHQFHRQ trust and its ultimate contribution to online purchase intentions of e-services (Gefen & Straub, 2003). $QRWKHUVWXG\LQGLFDWHGWKDWVHUYLFHFRQYHQLHQFH:HEVLWHVHUYLFHTXDOLW\DQGULVNDUHVLJQL¿FDQWIDF- tors affecting consumers’ satisfaction level, which in turn affects intention to use e-services (Zhang & Prybutok, 2005). C2C (Consumer-to-Consumer) E-Commerce C2C e-commerce supports direct business transactions and information exchange between and among consumers. While the transaction volume of C2C e-commerce is relatively new and behind B2C and B2B, it is growing rapidly. The most successful example of a C2C e-commerce application is online DXFWLRQ2WKHU&&DSSOLFDWLRQVLQFOXGHMREERDUGVDQG¿OHVKDULQJ&&FRPPHUFHH[LVWHGORQJEH- fore the advent of e-business. The oldest form of C2C commerce is bartering that existed before people started to use currency. More contemporary types of C2C commerce include newspaper advertisements and traditional auctions. While C2C e-commerce creates many opportunities such as disintermediation and an expanded pool of buyers and sellers, it also brings with it a number of challenges (Strader, T., & Ramaswami, S., 2002). These challenges include connecting buyers and sellers, trust issues, and issues with payment methods. While the main actors in C2C commerce are buyers and sellers, the presence of trusted interme- diaries is required to provide infrastructure for C2C e-commerce activities. Amazon.com and eBay are examples of successful C2C intermediaries whose revenue source is commissions paid by sellers. Reputation systems are used by C2C intermediaries to enhance trust. Unlike in B2C reputation systems where only consumers rate the e-commerce companies, evaluation in C2C reputation systems is two- way. Both buyers and sellers are able to rate their experience with the trading party. Then, evaluations DUHDYDLODEOHWRDOORWKHUXVHUV5HSXWDWLRQV\VWHPVDUHEHQH¿FLDOWRERWKEX\HUVDQGVHOOHUVWKH\FDQ be used to predict sellers’ behaviors (Bruce et al., 2004); they can be also used to predict the success of buyers’ transaction (Dholakia, 2005) Consumer-to-consumer (C2C) e-commerce is a growing area of e-commerce. While B2C auction has been widely studied, C2C auction has been understudied. When studying online auction, a phenomenon FDOOHGWKH³ZLQQHU¶VFXUVH´FDQRIWHQEHREVHUYHG$ZLQQHU¶VFXUVHPHDQVWKHEX\HUDQDXFWLRQZLQQHU HQGVXSSD\LQJPRUHWKDQWKHPLQLPXP¿[HGSULFHIRULGHQWLFDOSURGXFWV6WXGLHVVKRZWKDWQHDUO\ percent of auction participants suffer from the winner’s curse for certain commodities (Oh, 2002) xlii Three constructs from B2C research have been adapted to study satisfaction in C2C e-commerce (Jones & Leonard, 2007). The constructs include elements of the technology acceptance model (TAM), which includes perceived ease of use and usefulness; transaction cost analysis (TCA), which includes XQFHUWDLQW\DVVHWVSHFL¿FLW\DQGWLPHDQGVHUYLFHTXDOLW\6(5948$/ZKLFKLQFOXGHVUHOLDELOLW\ responsiveness, assurance, and empathy. The results show that TAM, TCA, and SERVQUAL all impact VDWLVIDFWLRQLQ&&HFRPPHUFH5HOLDELOLW\DQGUHVSRQVLYHQHVVLQÀXHQFHVDWLVIDFWLRQLQ&&HFRP- merce. ELECTRONIC BUSINESS DEVELOPMENT AND DESIGN METHODOLOGIES This section presents frameworks for e-business applications and design methodologies for e-business applications development. Frameworks for E-Business Applications Many of the information technology (IT) frameworks were developed before the advent of e-commerce. As electronic commerce grew rapidly and gained strategic importance, the existing IT infrastructure became inadequate in supporting the complex capabilities of e-business applications that need to lever- DJHLQWHU¿UPSURFHVVDQGIURQWHQGDQGEDFNHQGV\VWHPLQWHJUDWLRQ)XUWKHUPRUHGXHWRWKHYDULHW\ RIH[LVWLQJHEXVLQHVVPRGHOVLWVHHPVGLI¿FXOWWR¿QGDZLGHO\DFFHSWHGFDWHJRUL]DWLRQWKDWFDQEH analysed and assessed (Raisinghani et al., 2005). Bichler et al. (1998) are early researchers who in- vestigated the concept of component-based e-commerce technology as a solution to the e-commerce challenge at both system and application levels. Compared to a large pre-packed monolithic system equipped with all-encompassing features, component-based systems consist of a lightweight kernel to ZKLFKQHZIHDWXUHFRPSRQHQWVFDQEHDGGHGG\QDPLFDOO\5HDOL]LQJWKDWPDQ\¿UPVIDLOWRSURSHUO\SODQ WKHLUHFRPPHUFH,7LQIUDVWUXFWXUH5DJKXQDWKDQDQG0DGH\GHYHORSHGD¿UPOHYHOHEXVLQHVV management framework with six components: organizational e-commerce strategy, business processes transformation, information technology management, information management, customer management, and organizational knowledge management. The framework emphasizes component interrelationships to planning an e-business information systems infrastructure. In a similar modeling approach, an e-business architecture planning model was proposed with 12 generic e-business models and three axes of timeliness, FRPPXQLW\DQGLQWHJUDWLRQXSRQZKLFKGULYHUVRIWKHLQIRUPDWLRQDUFKLWHFWXUDOQHHGVRIHEXVLQHVV¿UPV fall. It was augmented with Sowa and Zachman’s Information Systems Architecture (1992) to further facilitate e-business information systems architecture planning (Pant and Ravichandran, 2001). As e-business places greater emphasis on IT infrastructure, IT managers need to assess infrastructure and prioritize investment options more carefully. Weill and Vitale (2002) investigated the relationships between e-business models (namely, content provider, direct to customer, full service provider, inter- mediary, value net integrator, virtual community, whole of enterprise, government) and nine main IT infrastructure services (applications infrastructure, communications, data management, IT management, security, architecture and standards, channel management, IT research and development, IT education). Their analysis indicated certain business models require different IT infrastructures. Design Methodologies for E-Business Applications Development Traditional IT development approaches such as systems development life-cycle (SDLC) methods and functional team IT organization were outdated long before the advent of the digital economy. In response xliii to the drawbacks of the traditional IT development approaches, various alternative systems development methods such as rapid application development (RAD) and object-oriented systems development were introduced in 1990s. While these new systems development methods, as well as the traditional IT de- velopment approaches, are used for e-business application projects, e-business application development presents unique challenges to systems designers. For example, there are differences in the preference for the type of virtual communities and the tool used by Chinese and U.S. communities, indicating that designers of global virtual communities must treat cultural differences with caution (Tan et al., 2006). Planning and designing an enterprise-wide database system for e-business is a critical task for the suc- cess of e-business oriented organizations (Yap, 2007). ,QWHUQHWFRPPHUFHGHYHORSPHQWPHWKRGRORJ\,&'0LGHQWL¿HVWKHPDLQUHTXLUHPHQWVRIPHWKRG- ologies for developing e-commerce applications and addresses a number of issues in many traditional V\VWHPVGHYHORSPHQWPHWKRGRORJLHVLGHQWL¿HGZLWKLQWKHOLWHUDWXUH6WDQGLQJ,&'0DGRSWVDQ HYROXWLRQDU\DSSURDFKWRV\VWHPVGHYHORSPHQWZKHUHVXEV\VWHPVSHFL¿FDSSURDFKHVFDQEHLQWHJUDWHG can address business and strategic focus, and can include a management structure as well as engineering aspects of e-commerce application development. Web quality metrics have been developed to evaluate the quality of web sites (Barnes & Vidgen, 2002). Developing web quality index and measuring web quality can help stakeholders understand and improve e-business web sites. Web quality metrics have been developed and validated through various e-business applications. Some of the dimensions include web site usability, information quality, and service interaction quality. WebQual is grounded in the perceptions of web site users, and analysis of data collected from users is used to guide web site design. (VHUYLFHVDUHLQWDQJLEOHDQGLWLVPRUHGLI¿FXOWWRGHVLJQDZHEVLWHIRUHVHUYLFHVWKDQSURGXFWRI- fering web sites (Baida et al., 2007). Furthermore, because most e-services involve multiple enterprises, creating a shared understanding of the service under consideration is a challenging task (Gordijn et al., 7KH¿UVWVWHSLQWKHHVHUYLFHVGHYHORSPHQWLVWRXQGHUVWDQGWKHHVHUYLFHLWVHOIEHIRUHWKH\FDQ build effective systems and support for these services. The complementary use of two requirements en- gineering techniques was recommended by using i * modeling, developers can explore strategic goals WKDW¿UPVKDYHDQGE\XVLQJH 3 YDOXHPRGHOLQJWKH\XQGHUVWDQGKRZWKHVHJRDOVFDQUHVXOWLQSUR¿WDEOH services for enterprises. ELECTRONIC BUSINESS TOOLS AND TECHNOLOGIES This section discusses web technology standards, agent technologies, and digital rights management (DRM) systems. Web Technology Standards One way to promote widespread adoption of e-business is to develop technology standards that can be XVHGWRPDNHLQIRUPDWLRQÀRZVHDPOHVVO\EHWZHHQDQGZLWKLQRUJDQL]DWLRQV([WHQVLEOH0DUNXS/DQ- guage (XML) is a simple, extendible text format derived from SGML. XML is extensible because it is QRWD¿[HGIRUPDWOLNH+70/HJGHYHORSHUVFDQFUHDWHWKHLURZQWDJVLQ;0/2ULJLQDOO\GHVLJQHG to meet the challenges of large-scale electronic publishing, XML allows the exchange of a wide variety of data on the Web and elsewhere and has become one of the key components in e-business systems integration (World Wide Web Consortium (W3C) Extensible Markup Language, 2008). A number of variations of XML have been developed. For example, eXtensible Business Reporting Language (XBRL) is an XML-based open standard which can be used to facilitate the process of the production, consump- xliv WLRQDQGH[FKDQJHRI¿QDQFLDOLQIRUPDWLRQDPRQJEXVLQHVVRUJDQL]DWLRQVVRIWZDUHDSSOLFDWLRQVDQG accounting standards (Troshani & Rao, 2007). Agent Technologies In the agent-mediated electronic commerce, the roles of agents for online consumers included product brokering, merchant brokering, and negotiation (Guan & Zhu, 2007). Other agent technologies also LGHQWL¿HGZHUHVXSSO\FKDLQPDQDJHPHQWDJHQWVPRELOHDJHQWVHYROXWLRQDU\DJHQWVDQGGDWDPLQLQJ agents. He et al. (2003) analyzed state of the art agent-mediated e-commerce. The roles of agents in %%HFRPPHUFHZHUHLGHQWL¿HGLQSDUWQHUVKLSIRUPDWLRQEURNHULQJDQGQHJRWLDWLRQ+DYLQJLGHQWL- ¿HGWKHUROHVIRUDJHQWVLQ%&DQG%%HFRPPHUFHVRPHRIWKHNH\XQGHUO\LQJWHFKQRORJLHVZHUH highlighted. Papazoglou (2001) studied basic characteristics of e-business agents. Two major characteristics discussed were that an agent must have a model of its own domain of expertise, and that an agent must have a model of the other agents that can provide relevant information. E-business agents that possess intelligence exhibit distinguishing characteristics: delegation abilities and communication ability. Agents were categorized into four types depending on their functionality and competencies: application agents, system-level support agents, personal agents, and general business activity agents. 1HJRWLDWLRQLVDSURPLVLQJ¿HOGZKHUHHOHFWURQLFDJHQWVFDQEHXVHG(OHFWURQLFQHJRWLDWLRQVFDQ involve simple bartering to complex negotiation strategies concerning multiple product and service packages. Combined negotiations are applied when negotiators are interested in many goods or services simultaneously and engage in multiple negotiations at the same time. A Combined Negotiation Support System (CNSS) called CONSENSUS was developed to help negotiators conduct multiple negotiations simultaneously, which without software support put a considerable burden of coordinating and reconciling multiple negotiations on the agents (Benyoucef et al., 2001). The architecture of CONSENSUS consists RIZRUNÀRZWHFKQRORJ\QHJRWLDWLQJVRIWZDUHDJHQWVDQGUXOHHQJLQHWHFKQRORJ\6HPDQWLF:HEWHFKQRO- ogy was applied to automated negotiation for which negotiation agents are deployed (Tamma, 2005). Negotiation protocols were not hard-coded in the negotiation agents, but were expressed in terms of a shared ontology with an explicit and declarative representation of the negotiation protocol. Since agents have very little prior knowledge of the protocol, they were designed to acquire negotiation knowledge directly from the marketplace. Digital Rights Management (DRM) Systems Online delivery of creative digital media, such as audio or video, is now an important part in the music and publishing industries. Due to the possibility of unlimited copying in the digital domain and breach of intellectual property rights, digital rights management (DRM) systems have been developed to control the way digital content is used (Hartung & Ramme, 2000). DRM systems typically incorporate access control technologies such as encryption, conditional access, copy control mechanisms, and media iden- WL¿FDWLRQDQGWUDFLQJPHFKDQLVPV'LJLWDOZDWHUPDUNLQJZKLFKLVWKHSURFHVVRIHPEHGGLQJDX[LOLDU\ information into a digital signal, is an essential component of modern DRM systems. Several water- marking methods are available including spread-spectrum, quantization, and amplitude modulation. To enhance the robustness of digital watermarking, a watermarking scheme that utilizes error correction codes based on the redundant residue number system was proposed (Goh & Siddiqi, 2007). xlv ELECTRONIC BUSINESS UTILIZATION AND APPLICATION This section discusses recommender systems, web personalization, web mining, organizational learning and knowledge management, and mobile computing. Recommender Systems 5HFRPPHQGHUV\VWHPVDUHEHLQJXVHGE\PDQ\LQQRYDWLYHHFRPPHUFH¿UPVVXFKDV$PD]RQFRP eBay, and Blockbuster to make personalized recommendations for products or services. Schafer et al. (2001) created taxonomy of recommender systems, based on a number of sources including the inputs required from the consumers, the additional knowledge required from the database, the ways the rec- ommendations are presented to consumers, the technologies used to create the recommendations, and the level of personalization of the recommendations. Recommender systems are very suitable for use in e-commerce due to easily obtainable consumer preference data. To overcome the information overload of online shoppers, a variety of recommendation methods have been developed including collaborative ¿OWHULQJFOXVWHUPRGHOVDQGVHDUFKEDVHGPHWKRGV &ROODERUDWLYH¿OWHULQJ&)LVWKHPRVWVXFFHVVIXOUHFRPPHQGDWLRQPHWKRGWRSHUIRUPDXWRPDWHG recommendations based upon the assumption that users, who had similar interests in the past, will have VLPLODULQWHUHVWVLQIXWXUHWRR7KHN1HDUHVW1HLJKERU.11PHWKRGLVXVHGLQ&)WR¿QGWKHNQHDUHVW neighbors for a given user and to employ them in predicting the user’s interests. However, this method suffers from data sparseness and scalability, and often leads to poor recommendations. A number of UHFRPPHQGDWLRQPHWKRGRORJLHVIRUWKHLGHQWL¿FDWLRQRIWKHQHDUHVWQHLJKERUVKDYHEHHQSURSRVHGWR enhance the recommendation quality and system performance of the CF-based recommender systems. A methodology was developed based on web usage mining and product taxonomy (Cho and Kim, 2004). Web usage mining tracks customers’ shopping behaviors on the web and the product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the customer preference database. Zeng et al. (2004) suggested a matrix conversion method to compress items into classes and an instance-selection method to narrow the scope of neighbor searching to improve WKHSHUIRUPDQFHRISUHGLFWLRQZLWKRXWVDFUL¿FLQJDFFXUDF\ One of the challenges of recommender systems is the need to adapt to an environment where users may have many completely different interests or may have completely different content. A hybrid CF PHWKRGZDVH[SORUHGE\FRPELQLQJ&)EDVHGRQLWHPDQGFROODERUDWLYH¿OWHULQJEDVHGRQXVHU/LHW al., 2005). Their algorithm analyzes the user-item matrix to identify similarity between the target item and other items, generates items similar to the target item, and determines neighbor users of the active user for the target item according to the similarity of other users to the active user based on similar items of the target item. Web Personalization E-business provided the ability to collect a vast amount of individual consumer data to the granularity of individual mouse movements. Coupled with the advances in one-to-one marketing strategies and Web mining technologies, e-business provided tremendous opportunities for web personalization. Web SHUVRQDOL]DWLRQLVWKHSURFHVVRIFXVWRPL]LQJDZHESDJHWRWKHQHHGVDQGSUHIHUHQFHVRIVSHFL¿FXVHUV XWLOL]LQJWKHNQRZOHGJHDFTXLUHGIURPWKHDQDO\VLVRIWKHXVHU¶VVHDUFKEHKDYLRUXVHUSUR¿OHDQGSUHIHU- ences. Since human involvement in the development of web personalization suffers from the problems of subjectivity and inadaptability, web mining has gained interest for web personalization in the areas xlvi of research and commerce. Web personalization techniques employed to analyze data collected from FXVWRPHUVLQFOXGHFRQWHQWEDVHG¿OWHULQJFROODERUDWLYH¿OWHULQJUXOHEDVHG¿OWHULQJDQGZHEXVDJH mining. A general architecture for automatic web personalization consists of: (1) the categorization and preprocessing of Web data, (2) the extraction of correlations between and across different kinds of data, and (3) the determination of the actions that should be recommended by a personalization system (Mobasher et al., 2000). Modules were presented that comprise a Web personalization system with an emphasis on the Web usage mining module, thus making the personalization process both automatic and dynamic, and hence up-to-date (Eirinaki and Vazirgiannis, 2003). They compared three different web usage mining tech- niques based on transaction clustering, usage clustering, and association rule discovery, to extract usage knowledge for the purpose of Web personalization. Combining the extracted knowledge with the current status of an ongoing web activity was also proposed to perform real-time personalization. 7KUHHZD\VRIWDLORULQJVHUYLFHVWRFXVWRPHUVH[LVW6DFNPDQQHWDO)LUVW¿UPVFDQRIIHU personalized services which build upon a one-to-one communication channel and utilize personal data for web personalization. Second, individualized services utilize the sequence of clicks, pages requested or items purchased, but do not require personal data. Since individualized services do not need to identify individual customers, they allow both improved shopping experience and consumers’ privacy. Third, universal services such as a product search engine or customer reputation systems need neither personal nor contextual data. Consumers can choose services dynamically for their needs. Privacy is a major concern for the success of personalization due to the extensive collection and use of personal and contextual data. Sackmann et al. (2006) suggested transparency with regard to the utilization of data is the only way to maintain privacy. The concept of privacy evidence was introduced as an initial step in this direction, as it permits an objective view into the data collected about a customer. Web Mining Web mining has become an important domain which aims to discover knowledge from the massive amount of transactional and clickstream data which e-commerce Web sites provide. Firms are utilizing web mining for web personalization, Web sites optimization, and one-to-one online marketing. Three web mining techniques have been widely used for e-commerce: content mining, structure mining, and usage mining. Web content mining focuses on the raw information available in Web pages and catego- rizes and/or ranks content of Web pages. Web structure mining focuses on the analysis of the structure of Web sites. Web sites are categorized based on the linking structure. Web usage mining focuses on the H[WUDFWLRQRINQRZOHGJHIURPVHUYHUORJ¿OHVFOLFNVWUHDPEHKDYLRUVDQGWUDQVDFWLRQDOGDWD When conducting a web mining task, the main decision is to choose a technique that is most suited for the problem domain under consideration. A number of data mining techniques have been suggested for web mining applications including: a divisive hierarchical clustering technique used to group Web site users according to their interests; sequential patterns technique used to predict the users behavior; DVVRFLDWLRQUXOHVXVHGWRDGGUHVVWKHSUREOHPRI&DFKLQJDQG3UHIHWFKLQJFODVVL¿FDWLRQWHFKQLTXHV used to classify Web sites to reorganize the Web site structure. No strict correlation was found between techniques and application domain (Facca & Lanzi, 2005). Practical guidelines for web mining usage include (Kohavi et al., 2004): Integrate data collection into operations to support analytics and experimentation and make it easy to transfer the collected information to a data warehouse; provide insight out-of-the-box and make it easy to derive insight and take action; provide simple reports and visualizations before building more complex models. Discussed challenges LQFOXGHWUDQVODWLQJEXVLQHVVTXHVWLRQVWRWKHGHVLUHGGDWDWUDQVIRUPDWLRQVGHYHORSLQJHI¿FLHQWDOJRULWKPV xlvii whose output is comprehensible and can handle multiple data types (dates, hierarchical attributes, dif- IHUHQWJUDQXODULW\GDWDDQGLQWHJUDWLQJZRUNÀRZIRUFRPSOH[PXOWLXVHUDQGPXOWLSURFHVVLQJZRUN environment. Organizational Learning and Knowledge Management Organizational learning and knowledge management are two integral parts of network enterprises, virtual enterprises, and other new IT-enabled organizational forms. E-business facilitates the process digitization RIRUJDQL]DWLRQDOOHDUQLQJDQGNQRZOHGJHPDQDJHPHQW+RZHYHU¿QGLQJHIIHFWLYHZD\VRIXWLOL]LQJH business tools and technologies for organizational learning and knowledge management is a challenge in the digital economy. Lin & Lee (2005) examined the impact of organizational learning factors (training available, technical expertise, and knowledge level) and knowledge management processes (knowledge acquisition, knowledge application, and knowledge sharing) on the e-business systems adoption level. Results of their survey showed that organizational learning factors and knowledge management processes are closely related to the level of e-business systems adoption. Knowledge management adds value in designing and managing e-business-enabled changes in or- ganizations (Beck, 2007; Ortega et al., 2008). In the digital economy, characterized by ubiquitous and RIWHQDXWRPDWHGLQIRUPDWLRQVKDULQJFDSDELOLWLHV¿UPVFDQXVHHEXVLQHVVNQRZOHGJHWRLPSURYHRUJD- nizational decision making, react more quickly to changes in the economic landscape, create dynamic custom content and consumer intimacy, and maintain competitive advantage (Warkentin et al., 2001). Leveraging e-business knowledge to enhance core business processes is the key to an organization’s success in deriving superior marketplace results. Through the use of e-business knowledge, organizations can achieve three critical tasks: (1) evaluate what type of work organizations are doing in the e-business environment (know-what); (2) understand how they are doing it (know-how); and (3) determine why certain practices and companies are likely to undergo change for the foreseeable future (know-why) (Fahey et al., 2001). Mobile Computing M-commerce ushered in a new wave of e-business evolution fueled by the increasing use of mobile devices such as cell phones and handheld devices. Although m-commerce can generally be considered to be an extension of electronic commerce, it has a number of unique characteristics and business models, as it embraces many emerging technologies. During the past several years, m-commerce has VLJQL¿FDQWO\FKDQJHGWKHZD\RUJDQL]DWLRQVGREXVLQHVVDQGHQWHUHGLQWRWKHFHQWHURIWKHHEXVLQHVV research and practices. Most m-commerce adopters are individuals who play the dual roles of technology user and service consumer. Coupled with the rapid advances in the wireless communications technolo- gies, m-commerce has enormous potential to become a dominant form of market mechanism. However, with m-commerce still in its infancy, we still need to explore opportunities and challenges posed by m-commerce, and identify the appropriate business models and business strategies for the success of m- commerce. In addition, the wireless networking infrastructure, W3C standards, and the open and global ZLUHOHVVDSSOLFDWLRQSURWRFRO:$3VSHFL¿FDWLRQQHHGWREHIXOO\HVWDEOLVKHGLQRUGHUWRWDNHDGYDQWDJH of m-commerce opportunities. The success of any mobile application requires a full understanding of application types, user requirements, technological constraints, and market potentials. The technology behind m-commerce and the products and services available were examined (Frolick DQG&KHQ7KH\DOVRH[DPLQHGWKHEHQH¿WVDQGFKDOOHQJHVRIPFRPPHUFHDVZHOODVWKHLVVXHV to be addressed when considering the implementation of m-commerce solutions. A number of studies xlviii addressed consumer perception and loyalty on m-commerce (Mahatanankoon et al., 2005; Mallat & Dahlberg, 2005; Jih, 2007). These studies reported that m-commerce consumer behaviors are similar to those of e-commerce, indicating that customer intention to use m-commerce is also affected by trust, habit, and customer satisfaction. Kim et al. (2007) adopted the theory of consumer choice and deci- sion making from economics and marketing research, and developed the Value-based Adoption Model (VAM), and explained customers’ m-commerce adoption from the value maximization perspective. The ¿QGLQJVGHPRQVWUDWHGWKDWFRQVXPHUV¶SHUFHSWLRQRIWKHYDOXHRIPFRPPHUFHLVDSULQFLSDOGHWHUPLQDQW of adoption intention. Organizational issues, key attributes in developing m-commerce, and the driving and impeding forces of m-commerce were also investigated (Bai et al., 2005). The unique characteristics of telecommunication markets along with the increasing trend for global e-business have led to a growing need for cross-national studies on m-commerce. Implications of m- commerce from a customer perspective across different countries were studied (Kim et al., 2004). Results IURPWKHRQOLQHVXUYH\LQWKUHHFRXQWULHV\LHOGHGWKHIROORZLQJ¿QGLQJV)LUVWDGRSWLRQSDWWHUQVIRUP commerce were different across the three countries. Second, customers’ perceptions of the importance RIWKHPFRPPHUFHDQGWKHLUZLOOLQJQHVVWRSD\IRUPFRPPHUFHVHUYLFHVGLIIHUHGVLJQL¿FDQWO\DPRQJ the different countries. Finally, customers’ preferred services in m-commerce also differed among the three countries. ORGANIZATIONAL AND SOCIAL IMPLICATIONS OF ELECTRONIC BUSINESS 7KLVVHFWLRQGLVFXVVHGVWUXFWXUDOFKDQJHVLQPDUNHWVLQWHU¿UPLQWHJUDWLRQYLUWXDOFRPPXQLWLHVGLJLWDO divide, and cybercrime. Structural Changes in Markets Structural changes in markets, such as disintermediation and re-intermediation have occurred due to WKHLPSDFWRIHFRPPHUFH$GHODDUH[DPLQHGWKHLQÀXHQFHRIHFRPPHUFHRQPDUNHWVWUXFWXUH and in particular the role of intermediaries. He observed that a purely economic perspective quickly over-generalizes the implications of e-commerce for the market structure. He suggested that a better understanding of the evolutionary impact of e-commerce on existing market structures and intermediary roles is achieved by taking into account both historical and regional perspectives. Inter-Firm Integration (EXVLQHVVKDVOHGWROHVVYHUWLFDOLQWHJUDWLRQDQGPRUHKRUL]RQWDOPHUJHUVDFTXLVLWLRQVDOOLDQFHVDV¿UPV specialize and partner with each other online. The Internet is facilitating less vertical integration due to lower transaction costs and globalization of sourcing in the vertical supply chain network. An example of this trend is the vertical disintegration of design, manufacturing, equipment production, and process development in the global semiconductor industry. Vertical disintegration has led to the rapid growth of semiconductor manufacturing capacity in Southeast Asia and the creation of new forms of international supply chain networks linking design and manufacturing specialists (Macher et al., 2002). The recent spate of horizontal mergers/acquisitions/alliances such as HP and Compaq, British Airline and American Airlines, and Chrysler and Daimler Benz may be attributable to the development e-com- merce technologies such as intranet and web-based groupware, which reduce agency costs dramatically. The horizontal integration leads to greater productivity and return on investment without incurring sig- xlix QL¿FDQWDJHQF\FRVWGLVHFRQRPLHV+RZHYHUWKHKRUL]RQWDOPHUJHUVDFTXLVLWLRQVDOOLDQFHVHIIHFWLYHO\ reliably and securely remain an important managerial challenge. Virtual Communities E-business has changed the way people interact with each other and enabled like-minded people to create virtual communities instantaneously from anywhere in the world. Virtual communities exhibit potential to become prominent business models structured around user interests and needs. Virtual communities can be an instrument for building relationships with customers, retaining customers’ loyalty, and generating PRUHSUR¿WV7DQHWDO$VYLUWXDOFRPPXQLWLHVJURZDWDQXQSUHFHGHQWHGUDWHLWLVLQFUHDVLQJO\ important for managers to understand the different types of virtual communities and community users, technological infrastructures, opportunities and challenges, and critical organizational and managerial issues such as communities-of-practice, virtual collaboration, and value creation. $¿HOGVXUYH\VKRZHGWKDWWKHOHYHORIFRPPXQLW\NQRZOHGJHVKDULQJDFWLYLW\LVSRVLWLYHO\UHODWHGWR YLUWXDOFRPPXQLW\RXWFRPHVDQGVXFKRXWFRPHVDUHVLJQL¿FDQWO\DVVRFLDWHGZLWKOR\DOW\WRWKHYLUWXDO community service provider, suggesting a need to integrate community knowledge sharing activity into a business activity in the form of an e-business model (Koh & Kim, 2004). Brand community is a special W\SHRIYLUWXDOFRPPXQLW\XVHGE\¿UPVDVDOHYHUIRUUHODWLRQVKLSPDUNHWLQJ$QGHUVHQ%UDQG communities not only provide companies with an additional communication channel but also allow the possibility of establishing linkages to devoted users. While certain marketing principles may still apply to virtual community marketing, marketers target- ing virtual communities should consider that community members: (1) are more active and discerning; (2) are less accessible to one-on-one processes, and (3) provide a wealth of valuable cultural information (Kozinets, 1999). Suggested strategies for effectively targeting more desirable types of virtual commu- nities and types of community members include: interaction-based segmentation, fragmentation-based segmentation, co-opting communities, paying-for-attention, and building networks by giving product DZD\7RUHDOL]HWKHSRWHQWLDOIRUVWUDWHJLFEHQH¿WVDQGPDUNHWRSSRUWXQLWLHVYLUWXDOFRPPXQLWLHVQHHG to develop a sound marketing strategy to improve their performance and to ensure strong value creation in the long term (Bughin & Hagel, 2000). Digital Divide While e-business transformed our society in many positive ways, some social scientists were beginning to carefully examine the policy implications of Internet access and usage by the general public and orga- nizations. Digital divide is an important issue for policymaking since it will affect access to technology, access to valuable information, individual learning, and organizational performance. For the general public, demographic variables like income, education, and race were major concerns because they are the most likely have a differential impact on the consequences of e-business for different segments in our society. For example, Gaps in general Web access and use between African-Americans and whites appear to be driven by whether or not there is a computer present in the home (Hoffman, et al., 2000). Digital divide also affects small and medium-sized enterprises (SMEs). Compared to large enterprises with strong IT infrastructures, SMEs face a number of barriers to the adoption of e-business technologies and techniques. The take-up of e-business by SMEs needs to be tempered with a more realistic view of KRZVPDOO¿UPVRSHUDWH7D\ORU0XUSK\ . VDWLVIDFWLRQLQ&&HFRPPHUFH5HOLDELOLWDQGUHVSRQVLYHQHVVLQÀXHQFHVDWLVIDFWLRQLQ&&HFRP- merce. ELECTRONIC BUSINESS DEVELOPMENT AND DESIGN METHODOLOGIES This section presents frameworks for e-business applications and design methodologies for e-business applications development. Frameworks. quality index and measuring web quality can help stakeholders understand and improve e-business web sites. Web quality metrics have been developed and validated through various e-business applications. . enterprises. ELECTRONIC BUSINESS TOOLS AND TECHNOLOGIES This section discusses web technology standards, agent technologies, and digital rights management (DRM) systems. Web Technology Standards One